UMAP plot
Cankun Wang
12/05/2022
1 Set working dir
## [1] "Current working directory: C:/Users/flyku/Documents/GitHub/BMBL-analysis-notebooks/figure_code"2 One group UMAP
DimPlot(combined,
reduction = "umap",
group.by = "seurat_clusters",
cols = cell_type_color,
label = T,
pt.size = 0.8)
2.1 Save to PNG
Idents(combined) <- combined$seurat_clusters
p0 <- DimPlot(
combined,
reduction = "umap",
cols = cell_type_color,
label = F,
pt.size = 0.4,
repel = T,
label.box = F
) + theme(
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank()
)
png(
paste0('./img/cluster_umap.png'),
width = 2500,
height = 1600,
res = 300
)
print(p0)
dev.off()## png
## 23 UMAP split by groups
DimPlot(combined,
reduction = "umap",
group.by = "cell_type",
split.by = "group",
cols = cell_type_color,
label = T,
pt.size = 0.8)
3.1 Save to PNG
## png
## 24 UMAP within circos plot
This circlize plot was inspired by the data visualization in a published paper (Figure1, https://www.nature.com/articles/s41586-021-03775-x) from Linnarsson’s lab.
nature example fig1
4.1 How to install
bioc_packages <- c("biomaRt", "GenomeInfoDb","EnsDb.Hsapiens.v86","GEOquery","simplifyEnrichment","ComplexHeatmap")
check_plot1cell <-
!("plot1cell" %in% installed.packages()[, "Package"])
if (check_plot1cell) {
bioc_packages <-
c(
"biomaRt",
"GenomeInfoDb",
"EnsDb.Hsapiens.v86",
"GEOquery",
"simplifyEnrichment",
"ComplexHeatmap"
)
dev_packages <- c("DoubletFinder", "hdf5r", "loomR")
dev.packages <-
c("chris-mcginnis-ucsf/DoubletFinder",
"Novartis/hdf5r",
"mojaveazure/loomR")
bioc_np <-
bioc_packages[!(bioc_packages %in% installed.packages()[, "Package"])]
dev_np <-
dev_packages[!(dev_packages %in% installed.packages()[, "Package"])]
if (!require("BiocManager"))
install.packages("BiocManager")
if (length(bioc_np)) {
BiocManager::install(bioc_np)
}
if (length(dev_np)) {
devtools::install_github(dev.packages[!(dev_packages %in% installed.packages()[, "Package"])])
}
devtools::install_github("TheHumphreysLab/plot1cell")
}plot_circlize <-
function (data_plot,
do.label = T,
contour.levels = c(0.2, 0.3),
pt.size = 0.5,
kde2d.n = 1000,
contour.nlevels = 100,
bg.color = "#F9F2E4",
col.use = NULL,
label.cex = 0.5,
repel = FALSE)
{
centers <-
data_plot %>% dplyr::group_by(Cluster) %>% summarise(x = median(x = x),
y = median(x = y))
z <- MASS::kde2d(data_plot$x, data_plot$y, n = kde2d.n)
celltypes <- names(table(data_plot$Cluster))
cell_colors <- (scales::hue_pal())(length(celltypes))
if (!is.null(col.use)) {
cell_colors = col.use
col_df <- data.frame(Cluster = celltypes, color2 = col.use)
cells_order <- rownames(data_plot)
data_plot <- merge(data_plot, col_df, by = "Cluster")
rownames(data_plot) <- data_plot$cells
data_plot <- data_plot[cells_order,]
data_plot$Colors <- data_plot$color2
}
circos.clear()
par(bg = bg.color)
circos.par(
cell.padding = c(0, 0, 0, 0),
track.margin = c(0.01,
0),
track.height = 0.01,
gap.degree = c(rep(2, (
length(celltypes) -
1
)), 12),
points.overflow.warning = FALSE
)
circos.initialize(sectors = data_plot$Cluster, x = data_plot$x_polar2)
circos.track(
data_plot$Cluster,
data_plot$x_polar2,
y = data_plot$dim2,
bg.border = NA,
panel.fun = function(x, y) {
circos.text(
CELL_META$xcenter,
CELL_META$cell.ylim[2] +
mm_y(4),
CELL_META$sector.index,
cex = 0.7,
col = "black",
facing = "bending.inside",
niceFacing = T,
)
circos.axis(labels.cex = 0.4,
col = "black",
labels.col = "black")
}
)
for (i in 1:length(celltypes)) {
dd <- data_plot[data_plot$Cluster == celltypes[i],]
circos.segments(
x0 = min(dd$x_polar2),
y0 = 0,
x1 = max(dd$x_polar2),
y1 = 0,
col = cell_colors[i],
lwd = 3,
sector.index = celltypes[i]
)
}
text(
x = 1,
y = 0.1,
labels = "Cell type",
cex = 0.7,
col = "black",
srt = -90
)
points(
data_plot$x,
data_plot$y,
pch = 19,
col = alpha(data_plot$Colors,
0.2),
cex = pt.size
)
contour(
z,
drawlabels = F,
nlevels = 100,
levels = contour.levels,
col = "#ae9c76",
add = TRUE
)
if (do.label) {
if (repel) {
textplot(
x = centers$x,
y = centers$y,
words = centers$Cluster,
cex = label.cex,
new = F,
show.lines = F
)
}
else {
text(
centers$x,
centers$y,
labels = centers$Cluster,
cex = label.cex,
col = "black"
)
}
}
}library(plot1cell)
###Prepare data for ploting
Idents(combined) <- combined$cell_type
circ_data <- prepare_circlize_data(combined, scale = 0.75)
color1<-rand_color(length(names(table(combined$group))))
color2<-rand_color(length(names(table(combined$sex))))
data_plot <- circ_data
###plot and save figures
png(
filename = './img/circlize_umap_plot.png',
width = 1800,
height = 1800,
res = 300
)
plot_circlize(
circ_data,
contour.levels = c(0.5),
contour.nlevels = 1000,
do.label = T,
pt.size = 0.5,
col.use = cell_type_color[seq_len(length(unique(Idents(combined))))],
bg.color = 'white',
kde2d.n = 1000,
repel = T,
label.cex = 0.7
)
add_track(circ_data,
group = "group",
colors = color1,
track_num = 2) ## can change it to one of the columns in the meta data of your seurat object
add_track(circ_data,
group = "sex",
colors = color2,
track_num = 3) ## can change it to one of the columns in the meta data of your seurat object
dev.off()## png
## 2
circos umap